{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Populating the interactive namespace from numpy and matplotlib\n"
     ]
    }
   ],
   "source": [
    "%pylab inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import cartopy.crs as ccrs\n",
    "import cartopy.feature as cfeature\n",
    "import datetime as dt\n",
    "\n",
    "import os"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "def llaverage(x):\n",
    "    if (x['Lon'].max() > 270.) and (x['Lon'].min() < 90.):\n",
    "        xd = x.copy()\n",
    "        xd['Lon'].where(xd['Lon'] < 180., xd['Lon']-360., inplace=True)\n",
    "        result = xd.mean()\n",
    "        if result['Lon'] < 0.: result['Lon'] = result['Lon']+360.\n",
    "        return result\n",
    "    else:\n",
    "        return x.mean()\n",
    "    return"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "pos_diri = r'C:\\Users\\apbarret\\Documents\\data\\SnowOnSeaIce\\SHEBA\\Position'\n",
    "pos_file = 'ship_spline_position_vel.txt'\n",
    "\n",
    "def date_parse(x):\n",
    "    return dt.datetime.strptime(x,'%y %m %d %H')\n",
    "\n",
    "names = ['Year', 'Month', 'Day', 'Hour', 'decidays', 'Lon', 'Lat', 'u', 'v']\n",
    "pos_df = pd.read_csv(os.path.join(pos_diri, pos_file), names=names, sep='\\s+',\n",
    "                     parse_dates={'time': ['Year', 'Month', 'Day', 'Hour']}, \n",
    "                     index_col='time')\n",
    "pos_df.index = [date_parse(d) for d in pos_df.index]\n",
    "pos_df['Lon'] = -1.*pos_df['Lon'] # Longitudes are degrees West"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {},
   "outputs": [],
   "source": [
    "mpos_df = pos_df.resample('MS').agg(llaverage)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0xc751588>]"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xc339c18>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "    map_proj = ccrs.NorthPolarStereo()\n",
    "\n",
    "    fig = plt.figure(figsize=(10,10))\n",
    "    ax = plt.subplot(projection=map_proj)\n",
    "    ax.set_extent([-180.,180.,65.,90.], ccrs.PlateCarree())\n",
    "    ax.add_feature(cfeature.LAND)\n",
    "    ax.gridlines()\n",
    "\n",
    "    traj0 = map_proj.transform_points(ccrs.PlateCarree(), pos_df['Lon'].values, pos_df['Lat'].values)\n",
    "    traj1 = map_proj.transform_points(ccrs.PlateCarree(), mpos_df['Lon'].values, mpos_df['Lat'].values)\n",
    "\n",
    "    ax.plot(traj0[:,0], traj0[:,1], '-', ms=2.5, label='Original')\n",
    "    ax.plot(traj1[:,0], traj1[:,1], '-', ms=2.5, label='Monthly mean')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Read SHEBA precipitation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>current_obs</th>\n",
       "      <th>previous_obs</th>\n",
       "      <th>time_firstevent</th>\n",
       "      <th>firstevent_type</th>\n",
       "      <th>cp</th>\n",
       "      <th>time_lastevent</th>\n",
       "      <th>lastevent_type</th>\n",
       "      <th>u10m</th>\n",
       "      <th>T10m</th>\n",
       "      <th>Pmeasured</th>\n",
       "      <th>Pcorrected</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1997-10-29</th>\n",
       "      <td>1997-10-29 20:00:00</td>\n",
       "      <td>1997-10-28 20:00:00</td>\n",
       "      <td>1997-10-29 12:00:00</td>\n",
       "      <td>38</td>\n",
       "      <td>1</td>\n",
       "      <td>1997-10-29 12:00:00</td>\n",
       "      <td>38</td>\n",
       "      <td>13.0</td>\n",
       "      <td>-12.9</td>\n",
       "      <td>0.40</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1997-10-30</th>\n",
       "      <td>1997-10-30 19:15:00</td>\n",
       "      <td>1997-10-29 20:00:00</td>\n",
       "      <td>1997-10-30 00:00:00</td>\n",
       "      <td>85</td>\n",
       "      <td>1</td>\n",
       "      <td>1997-10-30 12:00:00</td>\n",
       "      <td>85</td>\n",
       "      <td>14.3</td>\n",
       "      <td>-17.1</td>\n",
       "      <td>0.40</td>\n",
       "      <td>0.85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1997-10-31</th>\n",
       "      <td>1997-10-31 19:00:00</td>\n",
       "      <td>1997-10-30 19:15:00</td>\n",
       "      <td>1997-10-31 06:00:00</td>\n",
       "      <td>70</td>\n",
       "      <td>1</td>\n",
       "      <td>1997-10-31 06:00:00</td>\n",
       "      <td>70</td>\n",
       "      <td>7.0</td>\n",
       "      <td>-17.0</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1997-11-01</th>\n",
       "      <td>1997-11-01 20:00:00</td>\n",
       "      <td>1997-10-31 19:00:00</td>\n",
       "      <td>1997-11-01 00:00:00</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>1997-11-01 00:00:00</td>\n",
       "      <td>5</td>\n",
       "      <td>7.0</td>\n",
       "      <td>-20.5</td>\n",
       "      <td>0.20</td>\n",
       "      <td>0.24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1997-11-03</th>\n",
       "      <td>1997-11-03 20:13:00</td>\n",
       "      <td>1997-11-02 20:30:00</td>\n",
       "      <td>1997-11-03 12:00:00</td>\n",
       "      <td>70</td>\n",
       "      <td>1</td>\n",
       "      <td>1997-11-03 12:00:00</td>\n",
       "      <td>70</td>\n",
       "      <td>5.0</td>\n",
       "      <td>-24.4</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1997-11-04</th>\n",
       "      <td>1997-11-04 20:20:00</td>\n",
       "      <td>1997-11-03 20:13:00</td>\n",
       "      <td>1997-11-04 12:00:00</td>\n",
       "      <td>71</td>\n",
       "      <td>1</td>\n",
       "      <td>1997-11-04 18:00:00</td>\n",
       "      <td>70</td>\n",
       "      <td>1.5</td>\n",
       "      <td>-18.7</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1997-11-05</th>\n",
       "      <td>1997-11-05 20:00:00</td>\n",
       "      <td>1997-11-04 20:20:00</td>\n",
       "      <td>1997-11-05 18:00:00</td>\n",
       "      <td>38</td>\n",
       "      <td>1</td>\n",
       "      <td>1997-11-05 18:00:00</td>\n",
       "      <td>38</td>\n",
       "      <td>20.0</td>\n",
       "      <td>-16.6</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1997-11-06</th>\n",
       "      <td>1997-11-06 19:40:00</td>\n",
       "      <td>1997-11-05 20:00:00</td>\n",
       "      <td>1997-11-06 12:00:00</td>\n",
       "      <td>71</td>\n",
       "      <td>1</td>\n",
       "      <td>1997-11-06 18:00:00</td>\n",
       "      <td>71</td>\n",
       "      <td>15.5</td>\n",
       "      <td>-16.0</td>\n",
       "      <td>0.82</td>\n",
       "      <td>1.37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1997-11-08</th>\n",
       "      <td>1997-11-08 19:50:00</td>\n",
       "      <td>1997-11-07 20:15:00</td>\n",
       "      <td>1997-11-08 12:00:00</td>\n",
       "      <td>71</td>\n",
       "      <td>1</td>\n",
       "      <td>1997-11-08 00:00:00</td>\n",
       "      <td>73</td>\n",
       "      <td>7.5</td>\n",
       "      <td>-11.8</td>\n",
       "      <td>0.15</td>\n",
       "      <td>0.35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1997-11-11</th>\n",
       "      <td>1997-11-11 19:55:00</td>\n",
       "      <td>1997-11-10 19:44:00</td>\n",
       "      <td>1997-11-11 00:00:00</td>\n",
       "      <td>71</td>\n",
       "      <td>1</td>\n",
       "      <td>1997-11-11 18:00:00</td>\n",
       "      <td>71</td>\n",
       "      <td>15.0</td>\n",
       "      <td>-13.1</td>\n",
       "      <td>0.26</td>\n",
       "      <td>0.69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1997-11-12</th>\n",
       "      <td>1997-11-12 05:00:00</td>\n",
       "      <td>1997-11-11 19:55:00</td>\n",
       "      <td>1997-11-12 00:00:00</td>\n",
       "      <td>73</td>\n",
       "      <td>1</td>\n",
       "      <td>1997-11-12 00:00:00</td>\n",
       "      <td>73</td>\n",
       "      <td>12.0</td>\n",
       "      <td>-10.0</td>\n",
       "      <td>3.20</td>\n",
       "      <td>4.23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1997-11-12</th>\n",
       "      <td>1997-11-12 19:50:00</td>\n",
       "      <td>1997-11-12 05:00:00</td>\n",
       "      <td>1997-11-12 06:00:00</td>\n",
       "      <td>70</td>\n",
       "      <td>1</td>\n",
       "      <td>1997-11-12 18:00:00</td>\n",
       "      <td>71</td>\n",
       "      <td>9.3</td>\n",
       "      <td>-10.6</td>\n",
       "      <td>0.25</td>\n",
       "      <td>0.54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1997-11-13</th>\n",
       "      <td>1997-11-13 19:40:00</td>\n",
       "      <td>1997-11-12 19:50:00</td>\n",
       "      <td>1997-11-13 00:00:00</td>\n",
       "      <td>71</td>\n",
       "      <td>1</td>\n",
       "      <td>1997-11-13 12:00:00</td>\n",
       "      <td>71</td>\n",
       "      <td>18.7</td>\n",
       "      <td>-14.1</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1997-11-14</th>\n",
       "      <td>1997-11-14 19:45:00</td>\n",
       "      <td>1997-11-13 19:40:00</td>\n",
       "      <td>1997-11-14 12:00:00</td>\n",
       "      <td>71</td>\n",
       "      <td>1</td>\n",
       "      <td>1997-11-14 18:00:00</td>\n",
       "      <td>71</td>\n",
       "      <td>12.5</td>\n",
       "      <td>-13.3</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1997-11-21</th>\n",
       "      <td>1997-11-21 19:50:00</td>\n",
       "      <td>1997-11-19 19:52:00</td>\n",
       "      <td>1997-11-20 06:00:00</td>\n",
       "      <td>70</td>\n",
       "      <td>1</td>\n",
       "      <td>1997-11-21 18:00:00</td>\n",
       "      <td>70</td>\n",
       "      <td>5.5</td>\n",
       "      <td>-21.6</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1997-11-23</th>\n",
       "      <td>1997-11-23 19:58:00</td>\n",
       "      <td>1997-11-21 19:50:00</td>\n",
       "      <td>1997-11-22 00:00:00</td>\n",
       "      <td>71</td>\n",
       "      <td>1</td>\n",
       "      <td>1997-11-23 18:00:00</td>\n",
       "      <td>71</td>\n",
       "      <td>11.0</td>\n",
       "      <td>-17.7</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.71</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1997-11-24</th>\n",
       "      <td>1997-11-24 18:55:00</td>\n",
       "      <td>1997-11-23 19:58:00</td>\n",
       "      <td>1997-11-24 00:00:00</td>\n",
       "      <td>71</td>\n",
       "      <td>1</td>\n",
       "      <td>1997-11-24 06:00:00</td>\n",
       "      <td>71</td>\n",
       "      <td>11.0</td>\n",
       "      <td>-21.1</td>\n",
       "      <td>0.40</td>\n",
       "      <td>0.76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1997-11-25</th>\n",
       "      <td>1997-11-25 20:16:00</td>\n",
       "      <td>1997-11-24 18:55:00</td>\n",
       "      <td>1997-11-25 18:00:00</td>\n",
       "      <td>70</td>\n",
       "      <td>1</td>\n",
       "      <td>1997-11-25 18:00:00</td>\n",
       "      <td>70</td>\n",
       "      <td>4.0</td>\n",
       "      <td>-27.2</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1997-11-26</th>\n",
       "      <td>1997-11-26 21:01:00</td>\n",
       "      <td>1997-11-25 20:16:00</td>\n",
       "      <td>1997-11-26 12:00:00</td>\n",
       "      <td>71</td>\n",
       "      <td>1</td>\n",
       "      <td>1997-11-26 18:00:00</td>\n",
       "      <td>71</td>\n",
       "      <td>14.5</td>\n",
       "      <td>-19.4</td>\n",
       "      <td>0.20</td>\n",
       "      <td>0.49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1997-11-27</th>\n",
       "      <td>1997-11-27 20:00:00</td>\n",
       "      <td>1997-11-26 21:01:00</td>\n",
       "      <td>1997-11-27 00:00:00</td>\n",
       "      <td>70</td>\n",
       "      <td>1</td>\n",
       "      <td>1997-11-27 18:00:00</td>\n",
       "      <td>71</td>\n",
       "      <td>10.7</td>\n",
       "      <td>-23.4</td>\n",
       "      <td>0.10</td>\n",
       "      <td>0.39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1997-11-29</th>\n",
       "      <td>1997-11-29 23:35:00</td>\n",
       "      <td>1997-11-28 22:28:00</td>\n",
       "      <td>1997-11-29 06:00:00</td>\n",
       "      <td>70</td>\n",
       "      <td>1</td>\n",
       "      <td>1997-11-29 18:00:00</td>\n",
       "      <td>71</td>\n",
       "      <td>10.0</td>\n",
       "      <td>-23.8</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1997-11-30</th>\n",
       "      <td>1997-11-30 23:14:00</td>\n",
       "      <td>1997-11-29 23:35:00</td>\n",
       "      <td>1997-11-30 00:00:00</td>\n",
       "      <td>71</td>\n",
       "      <td>1</td>\n",
       "      <td>1997-11-30 06:00:00</td>\n",
       "      <td>71</td>\n",
       "      <td>10.0</td>\n",
       "      <td>-22.0</td>\n",
       "      <td>0.70</td>\n",
       "      <td>1.13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1997-12-02</th>\n",
       "      <td>1997-12-02 23:33:00</td>\n",
       "      <td>1997-12-01 23:05:00</td>\n",
       "      <td>1997-12-02 06:00:00</td>\n",
       "      <td>73</td>\n",
       "      <td>1</td>\n",
       "      <td>1997-12-02 18:00:00</td>\n",
       "      <td>71</td>\n",
       "      <td>21.7</td>\n",
       "      <td>-26.7</td>\n",
       "      <td>0.20</td>\n",
       "      <td>0.72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1997-12-03</th>\n",
       "      <td>1997-12-03 23:30:00</td>\n",
       "      <td>1997-12-02 23:33:00</td>\n",
       "      <td>1997-12-02 07:00:00</td>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "      <td>1997-12-02 07:00:00</td>\n",
       "      <td>7</td>\n",
       "      <td>22.0</td>\n",
       "      <td>-26.4</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1997-12-07</th>\n",
       "      <td>1997-12-07 03:00:00</td>\n",
       "      <td>1997-12-05 23:00:00</td>\n",
       "      <td>1997-12-06 03:00:00</td>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "      <td>1997-12-06 03:00:00</td>\n",
       "      <td>7</td>\n",
       "      <td>22.0</td>\n",
       "      <td>-24.1</td>\n",
       "      <td>3.10</td>\n",
       "      <td>6.92</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1997-12-07</th>\n",
       "      <td>1997-12-07 23:30:00</td>\n",
       "      <td>1997-12-07 03:00:00</td>\n",
       "      <td>1997-12-07 18:00:00</td>\n",
       "      <td>36</td>\n",
       "      <td>1</td>\n",
       "      <td>1997-12-07 18:00:00</td>\n",
       "      <td>36</td>\n",
       "      <td>20.0</td>\n",
       "      <td>-23.7</td>\n",
       "      <td>0.20</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1997-12-15</th>\n",
       "      <td>1997-12-15 00:40:00</td>\n",
       "      <td>1997-12-13 23:50:00</td>\n",
       "      <td>1997-12-14 06:00:00</td>\n",
       "      <td>72</td>\n",
       "      <td>1</td>\n",
       "      <td>1997-12-14 12:00:00</td>\n",
       "      <td>71</td>\n",
       "      <td>6.5</td>\n",
       "      <td>-24.1</td>\n",
       "      <td>0.90</td>\n",
       "      <td>1.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1997-12-20</th>\n",
       "      <td>1997-12-20 00:20:00</td>\n",
       "      <td>1997-12-19 00:00:00</td>\n",
       "      <td>1997-12-19 06:00:00</td>\n",
       "      <td>71</td>\n",
       "      <td>1</td>\n",
       "      <td>1997-12-19 06:00:00</td>\n",
       "      <td>71</td>\n",
       "      <td>3.0</td>\n",
       "      <td>-32.5</td>\n",
       "      <td>0.20</td>\n",
       "      <td>0.43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1997-12-22</th>\n",
       "      <td>1997-12-22 01:00:00</td>\n",
       "      <td>1997-12-21 00:00:00</td>\n",
       "      <td>1997-12-21 06:00:00</td>\n",
       "      <td>71</td>\n",
       "      <td>1</td>\n",
       "      <td>1997-12-21 12:00:00</td>\n",
       "      <td>76</td>\n",
       "      <td>5.0</td>\n",
       "      <td>-32.2</td>\n",
       "      <td>0.22</td>\n",
       "      <td>0.46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1997-12-23</th>\n",
       "      <td>1997-12-23 00:29:00</td>\n",
       "      <td>1997-12-22 01:00:00</td>\n",
       "      <td>1997-12-22 06:00:00</td>\n",
       "      <td>71</td>\n",
       "      <td>1</td>\n",
       "      <td>1997-12-23 00:00:00</td>\n",
       "      <td>71</td>\n",
       "      <td>12.2</td>\n",
       "      <td>-26.3</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1998-08-29</th>\n",
       "      <td>1998-08-29 19:13:00</td>\n",
       "      <td>1998-08-28 19:32:00</td>\n",
       "      <td>1998-08-29 00:00:00</td>\n",
       "      <td>77</td>\n",
       "      <td>1</td>\n",
       "      <td>1998-08-29 00:00:00</td>\n",
       "      <td>77</td>\n",
       "      <td>8.0</td>\n",
       "      <td>-0.6</td>\n",
       "      <td>0.60</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1998-09-01</th>\n",
       "      <td>1998-09-01 19:24:00</td>\n",
       "      <td>1998-08-31 18:58:00</td>\n",
       "      <td>1998-09-01 00:00:00</td>\n",
       "      <td>77</td>\n",
       "      <td>1</td>\n",
       "      <td>1998-09-01 00:00:00</td>\n",
       "      <td>77</td>\n",
       "      <td>13.0</td>\n",
       "      <td>-1.6</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1998-09-02</th>\n",
       "      <td>1998-09-02 18:57:00</td>\n",
       "      <td>1998-09-01 19:24:00</td>\n",
       "      <td>1998-09-02 15:00:00</td>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "      <td>1998-09-02 15:00:00</td>\n",
       "      <td>7</td>\n",
       "      <td>6.0</td>\n",
       "      <td>-3.3</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1998-09-03</th>\n",
       "      <td>1998-09-03 19:26:00</td>\n",
       "      <td>1998-09-02 18:57:00</td>\n",
       "      <td>1998-09-03 18:00:00</td>\n",
       "      <td>45</td>\n",
       "      <td>1</td>\n",
       "      <td>1998-09-03 18:00:00</td>\n",
       "      <td>45</td>\n",
       "      <td>6.0</td>\n",
       "      <td>-1.9</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1998-09-04</th>\n",
       "      <td>1998-09-04 19:16:00</td>\n",
       "      <td>1998-09-03 19:26:00</td>\n",
       "      <td>1998-09-03 07:00:00</td>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "      <td>1998-09-03 07:00:00</td>\n",
       "      <td>7</td>\n",
       "      <td>6.0</td>\n",
       "      <td>-2.5</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1998-09-05</th>\n",
       "      <td>1998-09-05 19:17:00</td>\n",
       "      <td>1998-09-04 19:16:00</td>\n",
       "      <td>1998-09-05 18:00:00</td>\n",
       "      <td>26</td>\n",
       "      <td>1</td>\n",
       "      <td>1998-09-05 18:00:00</td>\n",
       "      <td>26</td>\n",
       "      <td>11.0</td>\n",
       "      <td>-4.5</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1998-09-06</th>\n",
       "      <td>1998-09-06 19:13:00</td>\n",
       "      <td>1998-09-05 19:17:00</td>\n",
       "      <td>1998-09-06 06:00:00</td>\n",
       "      <td>73</td>\n",
       "      <td>1</td>\n",
       "      <td>1998-09-06 06:00:00</td>\n",
       "      <td>73</td>\n",
       "      <td>13.0</td>\n",
       "      <td>-1.4</td>\n",
       "      <td>1.10</td>\n",
       "      <td>1.70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1998-09-07</th>\n",
       "      <td>1998-09-07 19:35:00</td>\n",
       "      <td>1998-09-06 19:13:00</td>\n",
       "      <td>1998-09-07 18:00:00</td>\n",
       "      <td>85</td>\n",
       "      <td>1</td>\n",
       "      <td>1998-09-07 18:00:00</td>\n",
       "      <td>85</td>\n",
       "      <td>6.0</td>\n",
       "      <td>-1.9</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1998-09-13</th>\n",
       "      <td>1998-09-13 17:16:00</td>\n",
       "      <td>1998-09-12 18:54:00</td>\n",
       "      <td>1998-09-13 00:00:00</td>\n",
       "      <td>70</td>\n",
       "      <td>1</td>\n",
       "      <td>1998-09-13 06:00:00</td>\n",
       "      <td>70</td>\n",
       "      <td>7.0</td>\n",
       "      <td>-2.5</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1998-09-14</th>\n",
       "      <td>1998-09-14 17:32:00</td>\n",
       "      <td>1998-09-13 17:16:00</td>\n",
       "      <td>1998-09-13 18:00:00</td>\n",
       "      <td>70</td>\n",
       "      <td>1</td>\n",
       "      <td>1998-09-13 18:00:00</td>\n",
       "      <td>70</td>\n",
       "      <td>5.0</td>\n",
       "      <td>-2.8</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1998-09-17</th>\n",
       "      <td>1998-09-17 17:56:00</td>\n",
       "      <td>1998-09-16 17:30:00</td>\n",
       "      <td>1998-09-17 12:00:00</td>\n",
       "      <td>86</td>\n",
       "      <td>1</td>\n",
       "      <td>1998-09-17 12:00:00</td>\n",
       "      <td>86</td>\n",
       "      <td>20.0</td>\n",
       "      <td>-7.7</td>\n",
       "      <td>0.42</td>\n",
       "      <td>0.95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1998-09-18</th>\n",
       "      <td>1998-09-18 17:30:00</td>\n",
       "      <td>1998-09-17 17:56:00</td>\n",
       "      <td>1998-09-17 18:00:00</td>\n",
       "      <td>86</td>\n",
       "      <td>1</td>\n",
       "      <td>1998-09-17 18:00:00</td>\n",
       "      <td>86</td>\n",
       "      <td>17.0</td>\n",
       "      <td>-4.9</td>\n",
       "      <td>0.20</td>\n",
       "      <td>0.72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1998-09-19</th>\n",
       "      <td>1998-09-19 19:28:00</td>\n",
       "      <td>1998-09-18 17:30:00</td>\n",
       "      <td>1998-09-18 07:00:00</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>1998-09-18 07:00:00</td>\n",
       "      <td>5</td>\n",
       "      <td>16.0</td>\n",
       "      <td>-2.2</td>\n",
       "      <td>0.55</td>\n",
       "      <td>2.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1998-09-20</th>\n",
       "      <td>1998-09-20 18:10:00</td>\n",
       "      <td>1998-09-19 19:28:00</td>\n",
       "      <td>1998-09-20 18:00:00</td>\n",
       "      <td>87</td>\n",
       "      <td>1</td>\n",
       "      <td>1998-09-20 18:00:00</td>\n",
       "      <td>87</td>\n",
       "      <td>9.0</td>\n",
       "      <td>-3.2</td>\n",
       "      <td>0.20</td>\n",
       "      <td>0.36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1998-09-24</th>\n",
       "      <td>1998-09-24 17:22:00</td>\n",
       "      <td>1998-09-23 18:54:00</td>\n",
       "      <td>1998-09-24 06:00:00</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1998-09-24 06:00:00</td>\n",
       "      <td>2</td>\n",
       "      <td>3.0</td>\n",
       "      <td>-6.3</td>\n",
       "      <td>0.18</td>\n",
       "      <td>0.20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1998-09-25</th>\n",
       "      <td>1998-09-25 17:21:00</td>\n",
       "      <td>1998-09-24 17:22:00</td>\n",
       "      <td>1998-09-24 18:00:00</td>\n",
       "      <td>70</td>\n",
       "      <td>1</td>\n",
       "      <td>1998-09-25 12:00:00</td>\n",
       "      <td>70</td>\n",
       "      <td>5.5</td>\n",
       "      <td>-3.5</td>\n",
       "      <td>0.19</td>\n",
       "      <td>0.53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1998-09-26</th>\n",
       "      <td>1998-09-26 17:40:00</td>\n",
       "      <td>1998-09-25 17:21:00</td>\n",
       "      <td>1998-09-26 00:00:00</td>\n",
       "      <td>73</td>\n",
       "      <td>1</td>\n",
       "      <td>1998-09-26 12:00:00</td>\n",
       "      <td>73</td>\n",
       "      <td>7.0</td>\n",
       "      <td>-5.3</td>\n",
       "      <td>0.20</td>\n",
       "      <td>0.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1998-09-27</th>\n",
       "      <td>1998-09-27 17:18:00</td>\n",
       "      <td>1998-09-26 17:40:00</td>\n",
       "      <td>1998-09-27 06:00:00</td>\n",
       "      <td>73</td>\n",
       "      <td>1</td>\n",
       "      <td>1998-09-27 12:00:00</td>\n",
       "      <td>71</td>\n",
       "      <td>8.0</td>\n",
       "      <td>-7.0</td>\n",
       "      <td>0.20</td>\n",
       "      <td>0.45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1998-09-28</th>\n",
       "      <td>1998-09-28 19:22:00</td>\n",
       "      <td>1998-09-27 17:18:00</td>\n",
       "      <td>1998-09-28 06:00:00</td>\n",
       "      <td>26</td>\n",
       "      <td>1</td>\n",
       "      <td>1998-09-28 18:00:00</td>\n",
       "      <td>22</td>\n",
       "      <td>8.3</td>\n",
       "      <td>-8.5</td>\n",
       "      <td>0.60</td>\n",
       "      <td>0.93</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1998-09-29</th>\n",
       "      <td>1998-09-29 17:27:00</td>\n",
       "      <td>1998-09-28 19:22:00</td>\n",
       "      <td>1998-09-29 00:00:00</td>\n",
       "      <td>71</td>\n",
       "      <td>1</td>\n",
       "      <td>1998-09-29 12:00:00</td>\n",
       "      <td>73</td>\n",
       "      <td>7.3</td>\n",
       "      <td>-9.5</td>\n",
       "      <td>0.95</td>\n",
       "      <td>1.35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1998-09-30</th>\n",
       "      <td>1998-09-30 18:00:00</td>\n",
       "      <td>1998-09-29 17:27:00</td>\n",
       "      <td>1998-09-29 18:00:00</td>\n",
       "      <td>22</td>\n",
       "      <td>1</td>\n",
       "      <td>1998-09-30 12:00:00</td>\n",
       "      <td>71</td>\n",
       "      <td>5.5</td>\n",
       "      <td>-8.8</td>\n",
       "      <td>0.35</td>\n",
       "      <td>0.70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1998-10-01</th>\n",
       "      <td>1998-10-01 17:49:00</td>\n",
       "      <td>1998-09-30 18:00:00</td>\n",
       "      <td>1998-10-01 00:00:00</td>\n",
       "      <td>22</td>\n",
       "      <td>1</td>\n",
       "      <td>1998-10-01 12:00:00</td>\n",
       "      <td>71</td>\n",
       "      <td>11.3</td>\n",
       "      <td>-5.3</td>\n",
       "      <td>0.84</td>\n",
       "      <td>1.34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1998-10-02</th>\n",
       "      <td>1998-10-02 17:43:00</td>\n",
       "      <td>1998-10-01 17:49:00</td>\n",
       "      <td>1998-10-01 18:00:00</td>\n",
       "      <td>70</td>\n",
       "      <td>1</td>\n",
       "      <td>1998-10-02 06:00:00</td>\n",
       "      <td>76</td>\n",
       "      <td>5.7</td>\n",
       "      <td>-8.1</td>\n",
       "      <td>0.43</td>\n",
       "      <td>0.79</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1998-10-03</th>\n",
       "      <td>1998-10-03 17:50:00</td>\n",
       "      <td>1998-10-02 17:43:00</td>\n",
       "      <td>1998-10-03 00:00:00</td>\n",
       "      <td>41</td>\n",
       "      <td>1</td>\n",
       "      <td>1998-10-03 00:00:00</td>\n",
       "      <td>41</td>\n",
       "      <td>8.0</td>\n",
       "      <td>-10.3</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1998-10-04</th>\n",
       "      <td>1998-10-04 17:32:00</td>\n",
       "      <td>1998-10-03 17:50:00</td>\n",
       "      <td>1998-10-04 06:00:00</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1998-10-04 06:00:00</td>\n",
       "      <td>2</td>\n",
       "      <td>10.0</td>\n",
       "      <td>-21.1</td>\n",
       "      <td>0.18</td>\n",
       "      <td>0.21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1998-10-05</th>\n",
       "      <td>1998-10-05 17:14:00</td>\n",
       "      <td>1998-10-04 17:32:00</td>\n",
       "      <td>1998-10-04 18:00:00</td>\n",
       "      <td>76</td>\n",
       "      <td>1</td>\n",
       "      <td>1998-10-05 00:00:00</td>\n",
       "      <td>76</td>\n",
       "      <td>5.5</td>\n",
       "      <td>-21.3</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1998-10-06</th>\n",
       "      <td>1998-10-06 17:50:00</td>\n",
       "      <td>1998-10-05 17:14:00</td>\n",
       "      <td>1998-10-06 00:00:00</td>\n",
       "      <td>77</td>\n",
       "      <td>1</td>\n",
       "      <td>1998-10-06 12:00:00</td>\n",
       "      <td>75</td>\n",
       "      <td>20.0</td>\n",
       "      <td>-9.8</td>\n",
       "      <td>0.41</td>\n",
       "      <td>1.08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1998-10-07</th>\n",
       "      <td>1998-10-07 17:30:00</td>\n",
       "      <td>1998-10-06 17:50:00</td>\n",
       "      <td>1998-10-07 06:00:00</td>\n",
       "      <td>70</td>\n",
       "      <td>1</td>\n",
       "      <td>1998-10-07 12:00:00</td>\n",
       "      <td>76</td>\n",
       "      <td>13.5</td>\n",
       "      <td>-14.6</td>\n",
       "      <td>3.08</td>\n",
       "      <td>4.31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1998-10-08</th>\n",
       "      <td>1998-10-08 18:09:00</td>\n",
       "      <td>1998-10-07 17:30:00</td>\n",
       "      <td>1998-10-08 06:00:00</td>\n",
       "      <td>76</td>\n",
       "      <td>1</td>\n",
       "      <td>1998-10-08 06:00:00</td>\n",
       "      <td>76</td>\n",
       "      <td>13.0</td>\n",
       "      <td>-8.7</td>\n",
       "      <td>0.26</td>\n",
       "      <td>0.59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1998-10-09</th>\n",
       "      <td>1998-10-09 18:26:00</td>\n",
       "      <td>1998-10-08 18:09:00</td>\n",
       "      <td>1998-10-09 00:00:00</td>\n",
       "      <td>22</td>\n",
       "      <td>1</td>\n",
       "      <td>1998-10-09 00:00:00</td>\n",
       "      <td>22</td>\n",
       "      <td>19.0</td>\n",
       "      <td>-13.1</td>\n",
       "      <td>0.20</td>\n",
       "      <td>0.69</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>167 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                   current_obs        previous_obs     time_firstevent  \\\n",
       "1997-10-29 1997-10-29 20:00:00 1997-10-28 20:00:00 1997-10-29 12:00:00   \n",
       "1997-10-30 1997-10-30 19:15:00 1997-10-29 20:00:00 1997-10-30 00:00:00   \n",
       "1997-10-31 1997-10-31 19:00:00 1997-10-30 19:15:00 1997-10-31 06:00:00   \n",
       "1997-11-01 1997-11-01 20:00:00 1997-10-31 19:00:00 1997-11-01 00:00:00   \n",
       "1997-11-03 1997-11-03 20:13:00 1997-11-02 20:30:00 1997-11-03 12:00:00   \n",
       "1997-11-04 1997-11-04 20:20:00 1997-11-03 20:13:00 1997-11-04 12:00:00   \n",
       "1997-11-05 1997-11-05 20:00:00 1997-11-04 20:20:00 1997-11-05 18:00:00   \n",
       "1997-11-06 1997-11-06 19:40:00 1997-11-05 20:00:00 1997-11-06 12:00:00   \n",
       "1997-11-08 1997-11-08 19:50:00 1997-11-07 20:15:00 1997-11-08 12:00:00   \n",
       "1997-11-11 1997-11-11 19:55:00 1997-11-10 19:44:00 1997-11-11 00:00:00   \n",
       "1997-11-12 1997-11-12 05:00:00 1997-11-11 19:55:00 1997-11-12 00:00:00   \n",
       "1997-11-12 1997-11-12 19:50:00 1997-11-12 05:00:00 1997-11-12 06:00:00   \n",
       "1997-11-13 1997-11-13 19:40:00 1997-11-12 19:50:00 1997-11-13 00:00:00   \n",
       "1997-11-14 1997-11-14 19:45:00 1997-11-13 19:40:00 1997-11-14 12:00:00   \n",
       "1997-11-21 1997-11-21 19:50:00 1997-11-19 19:52:00 1997-11-20 06:00:00   \n",
       "1997-11-23 1997-11-23 19:58:00 1997-11-21 19:50:00 1997-11-22 00:00:00   \n",
       "1997-11-24 1997-11-24 18:55:00 1997-11-23 19:58:00 1997-11-24 00:00:00   \n",
       "1997-11-25 1997-11-25 20:16:00 1997-11-24 18:55:00 1997-11-25 18:00:00   \n",
       "1997-11-26 1997-11-26 21:01:00 1997-11-25 20:16:00 1997-11-26 12:00:00   \n",
       "1997-11-27 1997-11-27 20:00:00 1997-11-26 21:01:00 1997-11-27 00:00:00   \n",
       "1997-11-29 1997-11-29 23:35:00 1997-11-28 22:28:00 1997-11-29 06:00:00   \n",
       "1997-11-30 1997-11-30 23:14:00 1997-11-29 23:35:00 1997-11-30 00:00:00   \n",
       "1997-12-02 1997-12-02 23:33:00 1997-12-01 23:05:00 1997-12-02 06:00:00   \n",
       "1997-12-03 1997-12-03 23:30:00 1997-12-02 23:33:00 1997-12-02 07:00:00   \n",
       "1997-12-07 1997-12-07 03:00:00 1997-12-05 23:00:00 1997-12-06 03:00:00   \n",
       "1997-12-07 1997-12-07 23:30:00 1997-12-07 03:00:00 1997-12-07 18:00:00   \n",
       "1997-12-15 1997-12-15 00:40:00 1997-12-13 23:50:00 1997-12-14 06:00:00   \n",
       "1997-12-20 1997-12-20 00:20:00 1997-12-19 00:00:00 1997-12-19 06:00:00   \n",
       "1997-12-22 1997-12-22 01:00:00 1997-12-21 00:00:00 1997-12-21 06:00:00   \n",
       "1997-12-23 1997-12-23 00:29:00 1997-12-22 01:00:00 1997-12-22 06:00:00   \n",
       "...                        ...                 ...                 ...   \n",
       "1998-08-29 1998-08-29 19:13:00 1998-08-28 19:32:00 1998-08-29 00:00:00   \n",
       "1998-09-01 1998-09-01 19:24:00 1998-08-31 18:58:00 1998-09-01 00:00:00   \n",
       "1998-09-02 1998-09-02 18:57:00 1998-09-01 19:24:00 1998-09-02 15:00:00   \n",
       "1998-09-03 1998-09-03 19:26:00 1998-09-02 18:57:00 1998-09-03 18:00:00   \n",
       "1998-09-04 1998-09-04 19:16:00 1998-09-03 19:26:00 1998-09-03 07:00:00   \n",
       "1998-09-05 1998-09-05 19:17:00 1998-09-04 19:16:00 1998-09-05 18:00:00   \n",
       "1998-09-06 1998-09-06 19:13:00 1998-09-05 19:17:00 1998-09-06 06:00:00   \n",
       "1998-09-07 1998-09-07 19:35:00 1998-09-06 19:13:00 1998-09-07 18:00:00   \n",
       "1998-09-13 1998-09-13 17:16:00 1998-09-12 18:54:00 1998-09-13 00:00:00   \n",
       "1998-09-14 1998-09-14 17:32:00 1998-09-13 17:16:00 1998-09-13 18:00:00   \n",
       "1998-09-17 1998-09-17 17:56:00 1998-09-16 17:30:00 1998-09-17 12:00:00   \n",
       "1998-09-18 1998-09-18 17:30:00 1998-09-17 17:56:00 1998-09-17 18:00:00   \n",
       "1998-09-19 1998-09-19 19:28:00 1998-09-18 17:30:00 1998-09-18 07:00:00   \n",
       "1998-09-20 1998-09-20 18:10:00 1998-09-19 19:28:00 1998-09-20 18:00:00   \n",
       "1998-09-24 1998-09-24 17:22:00 1998-09-23 18:54:00 1998-09-24 06:00:00   \n",
       "1998-09-25 1998-09-25 17:21:00 1998-09-24 17:22:00 1998-09-24 18:00:00   \n",
       "1998-09-26 1998-09-26 17:40:00 1998-09-25 17:21:00 1998-09-26 00:00:00   \n",
       "1998-09-27 1998-09-27 17:18:00 1998-09-26 17:40:00 1998-09-27 06:00:00   \n",
       "1998-09-28 1998-09-28 19:22:00 1998-09-27 17:18:00 1998-09-28 06:00:00   \n",
       "1998-09-29 1998-09-29 17:27:00 1998-09-28 19:22:00 1998-09-29 00:00:00   \n",
       "1998-09-30 1998-09-30 18:00:00 1998-09-29 17:27:00 1998-09-29 18:00:00   \n",
       "1998-10-01 1998-10-01 17:49:00 1998-09-30 18:00:00 1998-10-01 00:00:00   \n",
       "1998-10-02 1998-10-02 17:43:00 1998-10-01 17:49:00 1998-10-01 18:00:00   \n",
       "1998-10-03 1998-10-03 17:50:00 1998-10-02 17:43:00 1998-10-03 00:00:00   \n",
       "1998-10-04 1998-10-04 17:32:00 1998-10-03 17:50:00 1998-10-04 06:00:00   \n",
       "1998-10-05 1998-10-05 17:14:00 1998-10-04 17:32:00 1998-10-04 18:00:00   \n",
       "1998-10-06 1998-10-06 17:50:00 1998-10-05 17:14:00 1998-10-06 00:00:00   \n",
       "1998-10-07 1998-10-07 17:30:00 1998-10-06 17:50:00 1998-10-07 06:00:00   \n",
       "1998-10-08 1998-10-08 18:09:00 1998-10-07 17:30:00 1998-10-08 06:00:00   \n",
       "1998-10-09 1998-10-09 18:26:00 1998-10-08 18:09:00 1998-10-09 00:00:00   \n",
       "\n",
       "            firstevent_type  cp      time_lastevent  lastevent_type  u10m  \\\n",
       "1997-10-29               38   1 1997-10-29 12:00:00              38  13.0   \n",
       "1997-10-30               85   1 1997-10-30 12:00:00              85  14.3   \n",
       "1997-10-31               70   1 1997-10-31 06:00:00              70   7.0   \n",
       "1997-11-01                5   1 1997-11-01 00:00:00               5   7.0   \n",
       "1997-11-03               70   1 1997-11-03 12:00:00              70   5.0   \n",
       "1997-11-04               71   1 1997-11-04 18:00:00              70   1.5   \n",
       "1997-11-05               38   1 1997-11-05 18:00:00              38  20.0   \n",
       "1997-11-06               71   1 1997-11-06 18:00:00              71  15.5   \n",
       "1997-11-08               71   1 1997-11-08 00:00:00              73   7.5   \n",
       "1997-11-11               71   1 1997-11-11 18:00:00              71  15.0   \n",
       "1997-11-12               73   1 1997-11-12 00:00:00              73  12.0   \n",
       "1997-11-12               70   1 1997-11-12 18:00:00              71   9.3   \n",
       "1997-11-13               71   1 1997-11-13 12:00:00              71  18.7   \n",
       "1997-11-14               71   1 1997-11-14 18:00:00              71  12.5   \n",
       "1997-11-21               70   1 1997-11-21 18:00:00              70   5.5   \n",
       "1997-11-23               71   1 1997-11-23 18:00:00              71  11.0   \n",
       "1997-11-24               71   1 1997-11-24 06:00:00              71  11.0   \n",
       "1997-11-25               70   1 1997-11-25 18:00:00              70   4.0   \n",
       "1997-11-26               71   1 1997-11-26 18:00:00              71  14.5   \n",
       "1997-11-27               70   1 1997-11-27 18:00:00              71  10.7   \n",
       "1997-11-29               70   1 1997-11-29 18:00:00              71  10.0   \n",
       "1997-11-30               71   1 1997-11-30 06:00:00              71  10.0   \n",
       "1997-12-02               73   1 1997-12-02 18:00:00              71  21.7   \n",
       "1997-12-03                7   2 1997-12-02 07:00:00               7  22.0   \n",
       "1997-12-07                7   2 1997-12-06 03:00:00               7  22.0   \n",
       "1997-12-07               36   1 1997-12-07 18:00:00              36  20.0   \n",
       "1997-12-15               72   1 1997-12-14 12:00:00              71   6.5   \n",
       "1997-12-20               71   1 1997-12-19 06:00:00              71   3.0   \n",
       "1997-12-22               71   1 1997-12-21 12:00:00              76   5.0   \n",
       "1997-12-23               71   1 1997-12-23 00:00:00              71  12.2   \n",
       "...                     ...  ..                 ...             ...   ...   \n",
       "1998-08-29               77   1 1998-08-29 00:00:00              77   8.0   \n",
       "1998-09-01               77   1 1998-09-01 00:00:00              77  13.0   \n",
       "1998-09-02                7   2 1998-09-02 15:00:00               7   6.0   \n",
       "1998-09-03               45   1 1998-09-03 18:00:00              45   6.0   \n",
       "1998-09-04                7   2 1998-09-03 07:00:00               7   6.0   \n",
       "1998-09-05               26   1 1998-09-05 18:00:00              26  11.0   \n",
       "1998-09-06               73   1 1998-09-06 06:00:00              73  13.0   \n",
       "1998-09-07               85   1 1998-09-07 18:00:00              85   6.0   \n",
       "1998-09-13               70   1 1998-09-13 06:00:00              70   7.0   \n",
       "1998-09-14               70   1 1998-09-13 18:00:00              70   5.0   \n",
       "1998-09-17               86   1 1998-09-17 12:00:00              86  20.0   \n",
       "1998-09-18               86   1 1998-09-17 18:00:00              86  17.0   \n",
       "1998-09-19                5   2 1998-09-18 07:00:00               5  16.0   \n",
       "1998-09-20               87   1 1998-09-20 18:00:00              87   9.0   \n",
       "1998-09-24                2   1 1998-09-24 06:00:00               2   3.0   \n",
       "1998-09-25               70   1 1998-09-25 12:00:00              70   5.5   \n",
       "1998-09-26               73   1 1998-09-26 12:00:00              73   7.0   \n",
       "1998-09-27               73   1 1998-09-27 12:00:00              71   8.0   \n",
       "1998-09-28               26   1 1998-09-28 18:00:00              22   8.3   \n",
       "1998-09-29               71   1 1998-09-29 12:00:00              73   7.3   \n",
       "1998-09-30               22   1 1998-09-30 12:00:00              71   5.5   \n",
       "1998-10-01               22   1 1998-10-01 12:00:00              71  11.3   \n",
       "1998-10-02               70   1 1998-10-02 06:00:00              76   5.7   \n",
       "1998-10-03               41   1 1998-10-03 00:00:00              41   8.0   \n",
       "1998-10-04                2   1 1998-10-04 06:00:00               2  10.0   \n",
       "1998-10-05               76   1 1998-10-05 00:00:00              76   5.5   \n",
       "1998-10-06               77   1 1998-10-06 12:00:00              75  20.0   \n",
       "1998-10-07               70   1 1998-10-07 12:00:00              76  13.5   \n",
       "1998-10-08               76   1 1998-10-08 06:00:00              76  13.0   \n",
       "1998-10-09               22   1 1998-10-09 00:00:00              22  19.0   \n",
       "\n",
       "            T10m  Pmeasured  Pcorrected  \n",
       "1997-10-29 -12.9       0.40        0.00  \n",
       "1997-10-30 -17.1       0.40        0.85  \n",
       "1997-10-31 -17.0       0.05        0.17  \n",
       "1997-11-01 -20.5       0.20        0.24  \n",
       "1997-11-03 -24.4       0.05        0.17  \n",
       "1997-11-04 -18.7       0.05        0.16  \n",
       "1997-11-05 -16.6       0.05        0.00  \n",
       "1997-11-06 -16.0       0.82        1.37  \n",
       "1997-11-08 -11.8       0.15        0.35  \n",
       "1997-11-11 -13.1       0.26        0.69  \n",
       "1997-11-12 -10.0       3.20        4.23  \n",
       "1997-11-12 -10.6       0.25        0.54  \n",
       "1997-11-13 -14.1       0.05        0.24  \n",
       "1997-11-14 -13.3       0.05        0.19  \n",
       "1997-11-21 -21.6       0.05        0.17  \n",
       "1997-11-23 -17.7       1.00        1.71  \n",
       "1997-11-24 -21.1       0.40        0.76  \n",
       "1997-11-25 -27.2       0.05        0.16  \n",
       "1997-11-26 -19.4       0.20        0.49  \n",
       "1997-11-27 -23.4       0.10        0.39  \n",
       "1997-11-29 -23.8       0.50        0.85  \n",
       "1997-11-30 -22.0       0.70        1.13  \n",
       "1997-12-02 -26.7       0.20        0.72  \n",
       "1997-12-03 -26.4       0.05        0.28  \n",
       "1997-12-07 -24.1       3.10        6.92  \n",
       "1997-12-07 -23.7       0.20        0.00  \n",
       "1997-12-15 -24.1       0.90        1.25  \n",
       "1997-12-20 -32.5       0.20        0.43  \n",
       "1997-12-22 -32.2       0.22        0.46  \n",
       "1997-12-23 -26.3       0.05        0.19  \n",
       "...          ...        ...         ...  \n",
       "1998-08-29  -0.6       0.60        1.00  \n",
       "1998-09-01  -1.6       0.50        0.99  \n",
       "1998-09-02  -3.3       0.05        0.17  \n",
       "1998-09-03  -1.9       0.05        0.00  \n",
       "1998-09-04  -2.5       0.05        0.17  \n",
       "1998-09-05  -4.5       0.05        0.19  \n",
       "1998-09-06  -1.4       1.10        1.70  \n",
       "1998-09-07  -1.9       0.50        0.69  \n",
       "1998-09-13  -2.5       0.05        0.17  \n",
       "1998-09-14  -2.8       0.05        0.17  \n",
       "1998-09-17  -7.7       0.42        0.95  \n",
       "1998-09-18  -4.9       0.20        0.72  \n",
       "1998-09-19  -2.2       0.55        2.00  \n",
       "1998-09-20  -3.2       0.20        0.36  \n",
       "1998-09-24  -6.3       0.18        0.20  \n",
       "1998-09-25  -3.5       0.19        0.53  \n",
       "1998-09-26  -5.3       0.20        0.50  \n",
       "1998-09-27  -7.0       0.20        0.45  \n",
       "1998-09-28  -8.5       0.60        0.93  \n",
       "1998-09-29  -9.5       0.95        1.35  \n",
       "1998-09-30  -8.8       0.35        0.70  \n",
       "1998-10-01  -5.3       0.84        1.34  \n",
       "1998-10-02  -8.1       0.43        0.79  \n",
       "1998-10-03 -10.3       0.05        0.00  \n",
       "1998-10-04 -21.1       0.18        0.21  \n",
       "1998-10-05 -21.3       0.05        0.17  \n",
       "1998-10-06  -9.8       0.41        1.08  \n",
       "1998-10-07 -14.6       3.08        4.31  \n",
       "1998-10-08  -8.7       0.26        0.59  \n",
       "1998-10-09 -13.1       0.20        0.69  \n",
       "\n",
       "[167 rows x 11 columns]"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ppt_diri = r'C:\\Users\\apbarret\\Documents\\data\\SnowOnSeaIce\\SHEBA\\Precipitation'\n",
    "ppt_file = 'nipher_data.corrected'\n",
    "\n",
    "def parse_timestamp(xx):\n",
    "    return [dt.datetime.strptime(str(x),'%y%m%d%H%M') for x in xx]\n",
    "\n",
    "names = ['current_obs', 'previous_obs', 'time_firstevent', 'firstevent_type',\n",
    "         'cp', 'time_lastevent', 'lastevent_type', 'u10m', 'T10m', 'Pmeasured', 'Pcorrected']\n",
    "df = pd.read_csv(os.path.join(ppt_diri, ppt_file), names=names, sep='\\s+')\n",
    "df['current_obs'] = parse_timestamp(df['current_obs'])\n",
    "df['previous_obs'] = parse_timestamp(df['previous_obs'])\n",
    "df['time_firstevent'] = parse_timestamp(df['time_firstevent'])\n",
    "df['time_lastevent'] = parse_timestamp(df['time_lastevent'])\n",
    "\n",
    "df.index = [dt.datetime.strptime(d.strftime('%Y%m%d'),'%Y%m%d') for d in df['current_obs']]\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Timedelta('4 days 00:13:00')"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['obs_period'] = df['current_obs'] - df['previous_obs']\n",
    "df['obs_period'].max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Pcorrected</th>\n",
       "      <th>days</th>\n",
       "      <th>Pmean</th>\n",
       "      <th>Tmean</th>\n",
       "      <th>Umean</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1997-10-01</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1997-11-01</th>\n",
       "      <td>13.84</td>\n",
       "      <td>30.0</td>\n",
       "      <td>0.461333</td>\n",
       "      <td>-18.173684</td>\n",
       "      <td>10.563158</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1997-12-01</th>\n",
       "      <td>10.42</td>\n",
       "      <td>31.0</td>\n",
       "      <td>0.336129</td>\n",
       "      <td>-28.166667</td>\n",
       "      <td>13.044444</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1998-01-01</th>\n",
       "      <td>20.54</td>\n",
       "      <td>31.0</td>\n",
       "      <td>0.662581</td>\n",
       "      <td>-23.170000</td>\n",
       "      <td>11.770000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1998-02-01</th>\n",
       "      <td>5.41</td>\n",
       "      <td>28.0</td>\n",
       "      <td>0.193214</td>\n",
       "      <td>-24.128571</td>\n",
       "      <td>9.128571</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1998-03-01</th>\n",
       "      <td>13.61</td>\n",
       "      <td>31.0</td>\n",
       "      <td>0.439032</td>\n",
       "      <td>-20.415789</td>\n",
       "      <td>9.857895</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1998-04-01</th>\n",
       "      <td>14.70</td>\n",
       "      <td>30.0</td>\n",
       "      <td>0.490000</td>\n",
       "      <td>-16.160000</td>\n",
       "      <td>9.200000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1998-05-01</th>\n",
       "      <td>9.90</td>\n",
       "      <td>31.0</td>\n",
       "      <td>0.319355</td>\n",
       "      <td>-9.985714</td>\n",
       "      <td>8.850000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1998-06-01</th>\n",
       "      <td>13.41</td>\n",
       "      <td>30.0</td>\n",
       "      <td>0.447000</td>\n",
       "      <td>-0.933333</td>\n",
       "      <td>9.222222</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1998-07-01</th>\n",
       "      <td>35.17</td>\n",
       "      <td>31.0</td>\n",
       "      <td>1.134516</td>\n",
       "      <td>-0.609091</td>\n",
       "      <td>9.963636</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1998-08-01</th>\n",
       "      <td>28.10</td>\n",
       "      <td>31.0</td>\n",
       "      <td>0.906452</td>\n",
       "      <td>-0.764706</td>\n",
       "      <td>10.070588</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1998-09-01</th>\n",
       "      <td>12.94</td>\n",
       "      <td>30.0</td>\n",
       "      <td>0.431333</td>\n",
       "      <td>-4.465000</td>\n",
       "      <td>8.980000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1998-10-01</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Pcorrected  days     Pmean      Tmean      Umean\n",
       "1997-10-01         NaN   NaN       NaN        NaN        NaN\n",
       "1997-11-01       13.84  30.0  0.461333 -18.173684  10.563158\n",
       "1997-12-01       10.42  31.0  0.336129 -28.166667  13.044444\n",
       "1998-01-01       20.54  31.0  0.662581 -23.170000  11.770000\n",
       "1998-02-01        5.41  28.0  0.193214 -24.128571   9.128571\n",
       "1998-03-01       13.61  31.0  0.439032 -20.415789   9.857895\n",
       "1998-04-01       14.70  30.0  0.490000 -16.160000   9.200000\n",
       "1998-05-01        9.90  31.0  0.319355  -9.985714   8.850000\n",
       "1998-06-01       13.41  30.0  0.447000  -0.933333   9.222222\n",
       "1998-07-01       35.17  31.0  1.134516  -0.609091   9.963636\n",
       "1998-08-01       28.10  31.0  0.906452  -0.764706  10.070588\n",
       "1998-09-01       12.94  30.0  0.431333  -4.465000   8.980000\n",
       "1998-10-01         NaN   NaN       NaN        NaN        NaN"
      ]
     },
     "execution_count": 145,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import calendar\n",
    "import numpy as np\n",
    "\n",
    "def agg_ppto(x):\n",
    "    print ('{:02d} {:d}'.format(x.index.month[0], x.size) )\n",
    "    \n",
    "moDf = df['Pcorrected'].resample('MS').sum().to_frame()\n",
    "\n",
    "moDf['days'] = [calendar.monthrange(y,m)[1] for y, m in zip(moDf.index.year, moDf.index.month)]\n",
    "moDf['Pmean'] = moDf['Pcorrected'] / moDf['days']\n",
    "moDf['Tmean'] = df['T10m'].resample('MS').mean()\n",
    "moDf['Umean'] = df['u10m'].resample('MS').mean()\n",
    "moDf.loc['1997-10-01',:] = np.nan\n",
    "moDf.loc['1998-10-01',:] = np.nan\n",
    "moDf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
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       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>decidays</th>\n",
       "      <th>Lon</th>\n",
       "      <th>Lat</th>\n",
       "      <th>u</th>\n",
       "      <th>v</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1997-10-01</th>\n",
       "      <td>288.979167</td>\n",
       "      <td>-143.889107</td>\n",
       "      <td>75.406523</td>\n",
       "      <td>-1.954305</td>\n",
       "      <td>2.389350</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1997-11-01</th>\n",
       "      <td>318.979167</td>\n",
       "      <td>-146.237366</td>\n",
       "      <td>76.088671</td>\n",
       "      <td>-3.594733</td>\n",
       "      <td>1.786369</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1997-12-01</th>\n",
       "      <td>349.479167</td>\n",
       "      <td>-149.664040</td>\n",
       "      <td>75.590619</td>\n",
       "      <td>-2.509832</td>\n",
       "      <td>-3.849589</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1998-01-01</th>\n",
       "      <td>380.479167</td>\n",
       "      <td>-151.799494</td>\n",
       "      <td>74.848145</td>\n",
       "      <td>-7.088679</td>\n",
       "      <td>-0.855763</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1998-02-01</th>\n",
       "      <td>409.979167</td>\n",
       "      <td>-158.178808</td>\n",
       "      <td>74.974938</td>\n",
       "      <td>-3.693109</td>\n",
       "      <td>0.491952</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1998-03-01</th>\n",
       "      <td>439.479167</td>\n",
       "      <td>-161.466655</td>\n",
       "      <td>75.616840</td>\n",
       "      <td>-4.765891</td>\n",
       "      <td>3.855237</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1998-04-01</th>\n",
       "      <td>469.979167</td>\n",
       "      <td>-165.251153</td>\n",
       "      <td>76.087511</td>\n",
       "      <td>-1.906940</td>\n",
       "      <td>-0.524390</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1998-05-01</th>\n",
       "      <td>500.479167</td>\n",
       "      <td>-166.131591</td>\n",
       "      <td>76.271072</td>\n",
       "      <td>-1.320854</td>\n",
       "      <td>3.367610</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1998-06-01</th>\n",
       "      <td>530.979167</td>\n",
       "      <td>-166.996181</td>\n",
       "      <td>77.276229</td>\n",
       "      <td>0.007646</td>\n",
       "      <td>5.471510</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1998-07-01</th>\n",
       "      <td>561.479167</td>\n",
       "      <td>-165.796649</td>\n",
       "      <td>78.251681</td>\n",
       "      <td>4.845049</td>\n",
       "      <td>1.509874</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1998-08-01</th>\n",
       "      <td>592.479167</td>\n",
       "      <td>-159.302322</td>\n",
       "      <td>78.924150</td>\n",
       "      <td>1.750290</td>\n",
       "      <td>4.869200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1998-09-01</th>\n",
       "      <td>622.979167</td>\n",
       "      <td>-163.048163</td>\n",
       "      <td>79.898835</td>\n",
       "      <td>-4.579955</td>\n",
       "      <td>2.788568</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1998-10-01</th>\n",
       "      <td>641.395834</td>\n",
       "      <td>-166.235613</td>\n",
       "      <td>80.228201</td>\n",
       "      <td>-0.595517</td>\n",
       "      <td>2.424529</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              decidays         Lon        Lat         u         v\n",
       "1997-10-01  288.979167 -143.889107  75.406523 -1.954305  2.389350\n",
       "1997-11-01  318.979167 -146.237366  76.088671 -3.594733  1.786369\n",
       "1997-12-01  349.479167 -149.664040  75.590619 -2.509832 -3.849589\n",
       "1998-01-01  380.479167 -151.799494  74.848145 -7.088679 -0.855763\n",
       "1998-02-01  409.979167 -158.178808  74.974938 -3.693109  0.491952\n",
       "1998-03-01  439.479167 -161.466655  75.616840 -4.765891  3.855237\n",
       "1998-04-01  469.979167 -165.251153  76.087511 -1.906940 -0.524390\n",
       "1998-05-01  500.479167 -166.131591  76.271072 -1.320854  3.367610\n",
       "1998-06-01  530.979167 -166.996181  77.276229  0.007646  5.471510\n",
       "1998-07-01  561.479167 -165.796649  78.251681  4.845049  1.509874\n",
       "1998-08-01  592.479167 -159.302322  78.924150  1.750290  4.869200\n",
       "1998-09-01  622.979167 -163.048163  79.898835 -4.579955  2.788568\n",
       "1998-10-01  641.395834 -166.235613  80.228201 -0.595517  2.424529"
      ]
     },
     "execution_count": 135,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mpos_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "metadata": {},
   "outputs": [],
   "source": [
    "mpos_df.join(moDf).dropna(subset=['Pcorrected'], axis=0).to_csv(os.path.join(ppt_diri,'nipher_data.corrected.month.csv'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0xeb12240>"
      ]
     },
     "execution_count": 132,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xeb131d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(15,7))\n",
    "moDf['Pcorrected'].plot.bar(ax=ax)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
